Adaptive learning generally encompasses an environment in which students receive their own personalized courses, made specifically for their strengths, weaknesses, goals, and engagement patterns. In some implementations, adaptive learning may use artificial intelligence to actively tailor course content to each student's needs, drawing upon knowledge domains as diverse as machine learning, cognitive science, predictive analytics, and educational theory.
While adaptive learning has shown promise when applied on an individual student basis, to date, adaptive training has not been effective for training groups of students. Many reasons may account for the lack of success applying adaptive training to a group training environment, including an inability to separate out individual contributions to the group. For example, past efforts to evaluate group performance have required multiple subject matter experts to observe trainees and record their observations and impressions. This approach is costly because it requires multiple human observers and can lead to subjective results due to personal bias or simply missing relevant actions due to distractions. For this and other reasons, adaptive learning not only may be very costly to implement, but also may not be effective in a group training environment.